Tissue Interactions that Regulate Facial Morphogenesis
Bibliographic record
Abstract
Molecular signaling among the forebrain, neural crest mesenchyme, and facial ectoderm regulate morphogenesis of the upper jaw. The forebrain and neural crest mesenchyme regulate expression of Sonic Hedgehog in the Frontonasal Ectodermal Zone (FEZ), which controls proximodistal extension and dorsoventral polarity of the upper jaw. The FEZ is highly conserved among vertebrates, but the spatial pattern of Shh expression is varied and highly correlated with the shape of the facial primordia in the developing upper jaw. In birds Shh expression spans the entire Frontonasal Process, while in mammals (human and mouse) Shh is restricted to lateral domains associated with the Median Nasal Processes. These expression patterns appear to result from molecular signals, including SHH itself, from the forebrain. From these observations we predicted that the signaling axis among the brain, mesenchyme, and ectoderm could be used to generate variation during evolutionary and disease processes. We have tested this by modulating SHH signaling in the brain of chick embryos to varying degrees and assessing facial shape using 3‐dimensional morphometrics and multivariate statistical analyses. This perturbation produced embryos with continuous phenotypic variation. The shape outcomes were highly correlated with SHH signaling, cell proliferation rates, and the organization of the FEZ in treated embryos. Further, these results revealed that phenotypic variation may result from non‐linear mechanisms by which signaling pathways operate. For instance, when the SHH pathway was blocked maximally, a highly penetrant phenotype resulted, while at lower levels of blockade the phenotypes rapidly returned to more normal shapes. Thus, large phenotypic variation was generated by small changes in receptor activation over certain ligand concentrations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".